Test¶

Templating demo using test image. templating is used to identify parts of the image that contain moss based on a template.

In [2]:

import cv2
import random
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec

import cv2 import random import numpy as np from matplotlib import pyplot as plt import matplotlib.gridspec as gridspec

In [3]:

image = cv2.imread("assets/test.jpg")
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), interpolation="nearest")

image = cv2.imread("assets/test.jpg") plt.figure(figsize=(10, 10)) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), interpolation="nearest")

Out[3]:

<matplotlib.image.AxesImage at 0x20e7b7295b0>

In [31]:

template = image.copy()
template = template[290:870, 862:1000, :]
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
(thresh, template) = cv2.threshold(template, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
print(thresh)
h, w = template.shape
plt.figure(figsize=(10, 10))
plt.imshow(template, cmap="gray", interpolation="nearest")

template = image.copy() template = template[290:870, 862:1000, :] template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) (thresh, template) = cv2.threshold(template, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) print(thresh) h, w = template.shape plt.figure(figsize=(10, 10)) plt.imshow(template, cmap="gray", interpolation="nearest")

115.0

Out[31]:

<matplotlib.image.AxesImage at 0x20e014324b0>

In [5]:

def recolour(image):
    delta_v = random.randint(-50, 50)
    delta_s = random.randint(-50, 50)
    delta_h = random.randint(-10, 10)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    hsv[:, :, 2] = cv2.add(hsv[:, :, 2], delta_v)
    hsv[:, :, 1] = cv2.add(hsv[:, :, 1], delta_s)
    hsv[:, :, 0] = cv2.add(hsv[:, :, 0], delta_h)
    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    return img


def wobble(img, ang_range, shear_range, trans_range):
    # Rotation
    ang_rot = np.random.uniform(ang_range) - ang_range / 2
    rows, cols, ch = img.shape
    Rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), ang_rot, 1)

    # Translation
    tr_x = trans_range * np.random.uniform() - trans_range / 2
    tr_y = trans_range * np.random.uniform() - trans_range / 2
    Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])

    # Shear
    pts1 = np.float32([[5, 5], [20, 5], [5, 20]])
    pt1 = 5 + shear_range * np.random.uniform() - shear_range / 2
    pt2 = 20 + shear_range * np.random.uniform() - shear_range / 2

    pts2 = np.float32([[pt1, 5], [pt2, pt1], [5, pt2]])
    shear_M = cv2.getAffineTransform(pts1, pts2)
    img = cv2.warpAffine(img, Rot_M, (cols, rows))
    img = cv2.warpAffine(img, Trans_M, (cols, rows))
    img = cv2.warpAffine(img, shear_M, (cols, rows))

    # Brightness
    img = recolour(img)

    return img


def plot_grid(images, figsize=(10, 10)):
    N = len(images)
    n = int(np.sqrt(N))
    gs1 = gridspec.GridSpec(n, n)
    gs1.update(wspace=0.02, hspace=0.02)  # set the spacing between axes.
    plt.figure(figsize=figsize)
    for i, img in enumerate(images):
        ax1 = plt.subplot(gs1[i])
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        ax1.set_aspect("equal")
        plt.subplot(n, n, i + 1)
        plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        plt.axis("off")

    plt.show()


candidates = [wobble(image.copy(), 2, 2, 2) for _ in range(16)]
plot_grid(candidates, figsize=(10, 10))

def recolour(image): delta_v = random.randint(-50, 50) delta_s = random.randint(-50, 50) delta_h = random.randint(-10, 10) hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) hsv[:, :, 2] = cv2.add(hsv[:, :, 2], delta_v) hsv[:, :, 1] = cv2.add(hsv[:, :, 1], delta_s) hsv[:, :, 0] = cv2.add(hsv[:, :, 0], delta_h) img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) return img def wobble(img, ang_range, shear_range, trans_range): # Rotation ang_rot = np.random.uniform(ang_range) - ang_range / 2 rows, cols, ch = img.shape Rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), ang_rot, 1) # Translation tr_x = trans_range * np.random.uniform() - trans_range / 2 tr_y = trans_range * np.random.uniform() - trans_range / 2 Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]]) # Shear pts1 = np.float32([[5, 5], [20, 5], [5, 20]]) pt1 = 5 + shear_range * np.random.uniform() - shear_range / 2 pt2 = 20 + shear_range * np.random.uniform() - shear_range / 2 pts2 = np.float32([[pt1, 5], [pt2, pt1], [5, pt2]]) shear_M = cv2.getAffineTransform(pts1, pts2) img = cv2.warpAffine(img, Rot_M, (cols, rows)) img = cv2.warpAffine(img, Trans_M, (cols, rows)) img = cv2.warpAffine(img, shear_M, (cols, rows)) # Brightness img = recolour(img) return img def plot_grid(images, figsize=(10, 10)): N = len(images) n = int(np.sqrt(N)) gs1 = gridspec.GridSpec(n, n) gs1.update(wspace=0.02, hspace=0.02) # set the spacing between axes. plt.figure(figsize=figsize) for i, img in enumerate(images): ax1 = plt.subplot(gs1[i]) ax1.set_xticklabels([]) ax1.set_yticklabels([]) ax1.set_aspect("equal") plt.subplot(n, n, i + 1) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis("off") plt.show() candidates = [wobble(image.copy(), 2, 2, 2) for _ in range(16)] plot_grid(candidates, figsize=(10, 10))

In [32]:

results = []
for img in candidates:
    bw_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    (thresh, bw_img) = cv2.threshold(bw_img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    res = cv2.matchTemplate(bw_img, template, cv2.TM_CCOEFF_NORMED)
    threshold = 0.7
    loc = np.where(res >= threshold)
    _img = img.copy()
    for pt in zip(*loc[::-1]):
        cv2.rectangle(_img, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
    results.append(_img)

plot_grid(results, figsize=(10, 10))

results = [] for img in candidates: bw_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) (thresh, bw_img) = cv2.threshold(bw_img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) res = cv2.matchTemplate(bw_img, template, cv2.TM_CCOEFF_NORMED) threshold = 0.7 loc = np.where(res >= threshold) _img = img.copy() for pt in zip(*loc[::-1]): cv2.rectangle(_img, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2) results.append(_img) plot_grid(results, figsize=(10, 10))

In [34]:

def check_clear(candidates, proposed, w, h):
    for pt in candidates:
        if pt[0] - w < proposed[0] < pt[0] + w and pt[1] - h < proposed[1] < pt[1] + h:
            return False
    return True


results = []
for img in candidates:
    bw_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    (thresh, bw_img) = cv2.threshold(bw_img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    res = cv2.matchTemplate(bw_img, template, cv2.TM_CCOEFF_NORMED)
    threshold = 0.5
    topk = 3
    loc = np.where(res >= threshold)
    h, w = template.shape
    _img = img.copy()
    hits = []
    for pt in zip(*loc[::-1]):
        if check_clear(hits, pt, w, h):
            hits.append(pt)
            cv2.rectangle(_img, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 10)
            if len(hits) == topk:
                break
    results.append(_img)

plot_grid(results, figsize=(10, 10))

def check_clear(candidates, proposed, w, h): for pt in candidates: if pt[0] - w < proposed[0] < pt[0] + w and pt[1] - h < proposed[1] < pt[1] + h: return False return True results = [] for img in candidates: bw_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) (thresh, bw_img) = cv2.threshold(bw_img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) res = cv2.matchTemplate(bw_img, template, cv2.TM_CCOEFF_NORMED) threshold = 0.5 topk = 3 loc = np.where(res >= threshold) h, w = template.shape _img = img.copy() hits = [] for pt in zip(*loc[::-1]): if check_clear(hits, pt, w, h): hits.append(pt) cv2.rectangle(_img, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 10) if len(hits) == topk: break results.append(_img) plot_grid(results, figsize=(10, 10))

In [ ]: